论文标题
具有潜在力模型的基因调节网络推断
Gene Regulatory Network Inference with Latent Force Models
论文作者
论文摘要
蛋白质合成的延迟在从RNA序列的时间序列数据中构造基因调节网络（GRN）时会产生混杂效应。在建模开发，疾病途径和药物副作用时，准确的GRN可能会非常有见地。我们提出了一个模型，该模型通过将机械方程和贝叶斯方法结合到实验数据中，结合了翻译延迟。这可以实现更大的生物学解释性，并且使用高斯过程可以通过内核以及自然考虑生物学变异的非线性表达。
Delays in protein synthesis cause a confounding effect when constructing Gene Regulatory Networks (GRNs) from RNA-sequencing time-series data. Accurate GRNs can be very insightful when modelling development, disease pathways, and drug side-effects. We present a model which incorporates translation delays by combining mechanistic equations and Bayesian approaches to fit to experimental data. This enables greater biological interpretability, and the use of Gaussian processes enables non-linear expressivity through kernels as well as naturally accounting for biological variation.
